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<div class="moz-cite-prefix">Hi,<br>
<br>
Sorry for the late reply.<br>
<br>
The problem is that your catch data has a large number of ages
that don't have tunning data. The default model for a4a was not
written for those cases. Check <br>
<br>
a4afit <- sca(gul0001, gul.indices)<br>
wireframe(data~year+age, data=harvest(a4afit))<br>
<br>
F in the last ages gets loose and the fit is quite poor.<br>
<br>
Once that you want to compare with XSA we can take the same kind
of approach, which is to force the oldest ages Fs the same. In
this case the model will fit one coefficient (times the year
coefficients) for ages older than 18, which mean they are fit
together, which I think is slightly different from XSA. Note that
you have a large +group in some years. <br>
<br>
This can be done using the "replace" method.<br>
<br>
fmod <- ~te(replace(age, age>18, 18), year, k = c(6, 10), bs
= "tp")<br>
a4afit <- sca(gul0001, gul.indices, fmodel=fmod)<br>
<br>
For comparison<br>
xsafit <- FLXSA(gul0001, gul.indices, FLXSA.control())<br>
<br>
wireframe(data~year+age|qname,
data=as.data.frame(FLQuants(a4a=harvest(a4afit),
xsa=xsafit@harvest)))<br>
<br>
Now, the one million dollars question is why you want to replicate
XSA ;)<br>
<br>
Best<br>
<br>
EJ<br>
<br>
ps: Take a look at the residuals and you'll see that both fits
have some odd residuals. In a4a you have a couple of simple
options to improve this fit, like including a year trend in the
catchability of the trawl cpue, etc.<br>
<br>
bubbles(age~year|qname, <a class="moz-txt-link-abbreviated" href="mailto:data=xsafit@index.res">data=xsafit@index.res</a>)<br>
plot(residuals(a4afit, gul0001, gul.indices))<br>
<br>
On 02/18/2015 02:35 PM, Havstovan FAMRI wrote:<br>
</div>
<blockquote
cite="mid:CAL09-Efooxo=vqZejD5+7k27ONP6so+=AAxd4Z9MnqoiXPFwXg@mail.gmail.com"
type="cite">
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<div class="gmail_signature">
<div>
<div>Hi,</div>
<div><br>
</div>
<div>Well that solved the problem (trimming the indices
object trim(gul.indices[[2]], age 4:12). No errors come
up but stock numbers and F's are really spurious</div>
<div>and nothing in the range of the XSA run. F are given
below as a example:</div>
<div><br>
</div>
<div>> gulfit@harvest[,ac(2010:2014)] # a4a output</div>
<div>An object of class "FLQuant"</div>
<div>, , unit = unique, season = all, area = unique</div>
<div><br>
</div>
<div> year</div>
<div>age 2010 2011 2012 2013 2014
</div>
<div> 4 0.00061883 0.00054841 0.00088312 0.00104010
0.00070947</div>
<div> 5 0.00194001 0.00170594 0.00221326 0.00254813
0.00223464</div>
<div> 6 0.00515556 0.00454088 0.00496769 0.00561751
0.00602334</div>
<div> 7 0.01007180 0.00901793 0.00900872 0.01006750
0.01202780</div>
<div> 8 0.01384780 0.01273870 0.01257350 0.01389800
0.01666770</div>
<div> 9 0.01448220 0.01365150 0.01385510 0.01499370
0.01669910</div>
<div> 10 0.01336920 0.01269240 0.01303890 0.01351880
0.01358130</div>
<div> 11 0.01248430 0.01165750 0.01145380 0.01107690
0.01019620</div>
<div> 12 0.01247860 0.01127330 0.00997400 0.00880635
0.00769486</div>
<div> 13 0.01285800 0.01125750 0.00874288 0.00700203
0.00595635</div>
<div> 14 0.01248820 0.01082000 0.00757566 0.00557152
0.00460538</div>
<div> 15 0.01064480 0.00939845 0.00633618 0.00439713
0.00344647</div>
<div> 16 0.00797521 0.00732193 0.00509501 0.00344610
0.00249670</div>
<div> 17 0.00569563 0.00541743 0.00403299 0.00273092
0.00182383</div>
<div> 18 0.00435670 0.00416232 0.00326465 0.00224600
0.00142545</div>
<div> 19 0.00388555 0.00355594 0.00278197 0.00194974
0.00124088</div>
<div> 20 0.00406418 0.00340829 0.00250123 0.00177887
0.00119688</div>
<div> 21 0.00465988 0.00349410 0.00231912 0.00166745
0.00122197</div>
<div><br>
</div>
<div>units: f</div>
<div>> gul_F[,ac(2010:2014)] # XSA output</div>
<div> 2010 2011 2012 2013 2014</div>
<div>4 0.0047 0.0115 0.0015 0.0066 0.0053</div>
<div>5 0.0143 0.0311 0.0100 0.0154 0.0296</div>
<div>6 0.0675 0.0787 0.0485 0.0655 0.0872</div>
<div>7 0.1257 0.1506 0.0947 0.1182 0.1404</div>
<div>8 0.1696 0.2211 0.1632 0.1897 0.2082</div>
<div>9 0.2107 0.2428 0.1669 0.2651 0.2607</div>
<div>10 0.2624 0.3209 0.2170 0.2783 0.2700</div>
<div>11 0.2686 0.3086 0.2396 0.3374 0.2840</div>
<div>12 0.3309 0.4213 0.2750 0.3371 0.2146</div>
<div>13 0.4980 0.6288 0.4110 0.4805 0.2861</div>
<div>14 0.4428 0.5557 0.4666 0.5562 0.3298</div>
<div>15 0.4462 0.5266 0.4679 0.6606 0.4605</div>
<div>16 0.3444 0.4555 0.4277 0.5946 0.4433</div>
<div>17 0.2812 0.3540 0.3561 0.5347 0.3910</div>
<div>18 0.2529 0.3157 0.3512 0.4841 0.3601</div>
<div>19 0.4566 0.2772 0.1803 0.2773 0.3215</div>
<div>20 0.2534 0.3124 0.2804 0.4170 0.4099</div>
<div>21 0.2534 0.3124 0.2804 0.4170 0.4099</div>
<div><br>
</div>
<div>best,</div>
<div>Luis</div>
</div>
<div><br>
</div>
</div>
</div>
</div>
<br>
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<pre class="moz-signature" cols="72">--
Ernesto Jardim<a class="moz-txt-link-rfc2396E" href="mailto:ernesto.jardim@jrc.ec.europa.eu"><ernesto.jardim@jrc.ec.europa.eu></a>
Fisheries Scientist
FISHREG – Scientific Support to Fisheries
IPSC Maritime Affairs Unit
EC Joint Research Center
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I-21027 Ispra (VA), Italy
Office : +39 0332 785311
Fax: +39 0332 789658
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